import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

%matplotlib inline

data = pd.read_csv("diabetes2.csv")

data.head()

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
0	6	148	72	35	0	33.6	0.627	50	1
1	1	85	66	29	0	26.6	0.351	31	0
2	8	183	64	0	0	23.3	0.672	32	1
3	1	89	66	23	94	28.1	0.167	21	0
4	0	137	40	35	168	43.1	2.288	33	1

data_temp = data

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 9 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Pregnancies               768 non-null    int64  
 1   Glucose                   768 non-null    int64  
 2   BloodPressure             768 non-null    int64  
 3   SkinThickness             768 non-null    int64  
 4   Insulin                   768 non-null    int64  
 5   BMI                       768 non-null    float64
 6   DiabetesPedigreeFunction  768 non-null    float64
 7   Age                       768 non-null    int64  
 8   Outcome                   768 non-null    int64  
dtypes: float64(2), int64(7)
memory usage: 54.1 KB

data.shape

(768, 9)

data['Outcome'].value_counts()

0    500
1    268
Name: Outcome, dtype: int64

data.isnull().sum()

Pregnancies                 0
Glucose                     0
BloodPressure               0
SkinThickness               0
Insulin                     0
BMI                         0
DiabetesPedigreeFunction    0
Age                         0
Outcome                     0
dtype: int64

sns.catplot(x="Outcome",kind="count",data=data)
plt.show()

# The visulization count of Age of their Diabetics
ax = sns.catplot(x="Age",kind="count",hue="Outcome",data=data,palette="pastel",legend=False)
ax.fig.set_figwidth(20)
ax.fig.set_figheight(10)
plt.legend(loc="upper right",labels=["Non diabetic","Diabetic"])
plt.show()

# Age Distribution by outcome 0

fig = px.histogram(data,x=data[data["Outcome"]==0].Age,marginal="box",color_discrete_sequence=['lightgreen'])
fig.show()

# Age distribution by Outcome 0
fig = px.histogram(data, x=data[data.Outcome==1].Age,
                   marginal="box",
                   color_discrete_sequence=['red'])
fig.show()

data[data['Outcome']==1].Glucose.mean()

141.25746268656715

x = data.drop(['Outcome'],axis=1)
y = data.loc[:,"Outcome"].values

from sklearn.model_selection import train_test_split
xtrain, xtest, ytrain, ytest = train_test_split(x,y,test_size = 0.33, random_state= 123)

print("xtrain",xtrain.shape)
print("ytrain",ytrain.shape)
print("xtest",xtest.shape)
print("ytest",ytest.shape)

xtrain (514, 8)
ytrain (514,)
xtest (254, 8)
ytest (254,)

from sklearn.linear_model import LogisticRegression
model = LogisticRegression(solver='liblinear', max_iter = 1000)
model.fit(xtrain,ytrain)

LogisticRegression(max_iter=1000, solver='liblinear')

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

xpred = model.predict(xtrain)

from sklearn.metrics import confusion_matrix
confusion_matrix(ytrain,xpred)

array([[312,  31],
       [ 90,  81]], dtype=int64)

#train score
from sklearn.metrics import accuracy_score
score = accuracy_score(ytrain, xpred)
score

0.7645914396887159

ypred = model.predict(xtest)

confusion_matrix(ypred, ytest)

array([[143,  38],
       [ 14,  59]], dtype=int64)

#test score
accuracy_score(ypred, ytest)

0.7952755905511811

cm1 = confusion_matrix(ytest, ypred)
sns.heatmap(cm1, annot=True, fmt=".0f")
plt.xlabel("Predicted Values")
plt.ylabel("Actual Values")
plt.title("Accuracy Score:{0}".format(score),size=15)
plt.show()

from sklearn.metrics import classification_report,accuracy_score,f1_score,precision_score,recall_score,roc_curve,roc_auc_score
print(classification_report(ytest,ypred))

              precision    recall  f1-score   support

           0       0.79      0.91      0.85       157
           1       0.81      0.61      0.69        97

    accuracy                           0.80       254
   macro avg       0.80      0.76      0.77       254
weighted avg       0.80      0.80      0.79       254

test_data = [[0,150,33.7,50,150,74,0.5,53]]

testData = pd.DataFrame(test_data,columns = ['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','BMI','DiabetesPedigreeFunction','Age'])

result = model.predict(testData)
result[0]

1

import joblib
joblib.dump(model,"diabetics.pkl")

['diabetics.pkl']